Phoneme classification using naive Bayes classifier in reconstructed phase space

Author

Ye, Jinjin ; Povinelli, Richard J. ; Johnson, Matthew Thomas

Author_Institution

Dept. of Electr. & Comput. Eng., Marquette Univ., Milwaukee, WI, USA

fYear

2002

fDate

13-16 Oct. 2002

Firstpage

37

Lastpage

40

Abstract

A novel method for classifying speech phonemes is presented. Unlike traditional cepstral based methods, this approach uses histograms of reconstructed phase spaces. A naive Bayes classifier uses the probability mass estimates for classification. The approach is verified using isolated fricative, vowel, and nasal phonemes from the TIMIT corpus. The results show that a reconstructed phase space approach is a viable method for classification of phonemes, with the potential for use in a continuous speech recognition system.

Keywords

Bayes methods; Gaussian processes; phase space methods; signal classification; signal reconstruction; speech processing; speech recognition; Bayes classifier; Gaussian mixture models; histograms; phoneme classification; probability mass estimates; reconstructed phase space approach; speech phonemes; speech recognition system; Cepstral analysis; Delay effects; Heart; Hidden Markov models; Histograms; Linear predictive coding; Neural networks; Spectral analysis; Speech analysis; Speech recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Signal Processing Workshop, 2002 and the 2nd Signal Processing Education Workshop. Proceedings of 2002 IEEE 10th

Print_ISBN

0-7803-8116-5

Type

conf

DOI

10.1109/DSPWS.2002.1231072

Filename

1231072